Clustering and dendrogram
Sex seems to be the determinng factor in hierarchical clustering. Neither disease status nor ethnicity seem to be clustered in any meaningful manner. Also, one sample seems to have a mismatched ‘sex’ label.
Heatmap
Heatmap with the same clustering. Highly distant groups in rows are separated by sex.
PCA
Screeplot
It would take 119 principal components to capture 90% of variance in the data.
Pair plot
Sex by colors. Disease status by shape. The sample with a mismatched ‘sex’ label is visible here too.
PC Heatmap
The heatmap of first 7 PCs.
=======The heatmap of principal component scores doesn’t reveal much.
>>>>>>> 68a13adc78dc95f27af127cf4919c6d1de051d48PC relation with metadata
Seems like the only relevant characterictis separated by first 5 PCs is sex.
Sex
Disease Status
Age
The lighter the point, the higher the age.
Ethnicity
